Federated Image-Based Localization

• Federated image-based localization is a paradigm where distributed agents use local visual data and priors to collaboratively determine spatial positions while maintaining privacy.
• It integrates deep learning, probabilistic pose estimation, and optimization frameworks to fuse local geometric features with global priors, ensuring robust and scalable results.
• Privacy-preserving techniques such as hierarchical federated learning and similarity-aware aggregation reduce communication overhead and address data heterogeneity across diverse networks.

Federated image-based localization is a computational paradigm in which distributed agents or servers collaborate to determine their spatial position by leveraging visual information in concert with local priors, shared representations, and decentralized learning or decision-making processes. Unlike traditional centralized localization systems—which aggregate all data (images, maps, descriptors) in a single repository—federated methods enable privacy-aware, scalable, and robust location inference across heterogeneous networks, diverse spatial domains, and variable infrastructure. These approaches span supervised, optimization-based, and learning-based architectures, commonly integrating principles from probabilistic modeling, graph theory, deep learning, and consensus optimization.

1. Map Representation and Geometric Feature Fusion

Modern federated localization extends conventional visual place recognition and image retrieval into distributed contexts by organizing reference images or geometric information into compact, communicable forms. For instance, semantic image-based geolocation (Mousavian et al., 2016) leverages a sparse set of geo-tagged views and a 2D map with building identities, associating image regions to map features by projecting planar facade hypotheses using vanishing point detection and semantic segmentation. This results in automatic assignment of landmarks to image pixels and computation of pose likelihoods via normalized similarity metrics over facade extents and orientations.

Further, network flow frameworks (Thoma et al., 2018) operationalize the selection of reference landmarks: images are abstracted into nodes of a graph, edges quantifying both geometric adjacency and visual similarity (features extracted via NetVLAD or VGG layers), and flows are optimized via quadratic or second-order cone programming to satisfy spatial, visual, compactness, and coverage constraints. Such frameworks allow distributed map summarization, where agents maintain local landmark sets and coordinate through compact information exchange versus raw image sharing.

Some methods, such as SuperGF (Song et al., 2022), unify local (keypoint, descriptor) and global (scene-level) features using transformer-based aggregation modules, enabling flexible extraction and fusion of image matching-specific and retrieval-specific descriptors. In federated settings, such architectures permit client-side feature computation with only aggregative or pooled representations transmitted, thus maintaining privacy and bandwidth efficiency.

2. Likelihood Computation and Probabilistic Pose Estimation

Probabilistic reasoning underpins many federated localization strategies, especially when local observations must be integrated with global priors or schematic maps. In semantic geolocation (Mousavian et al., 2016), pose likelihood maps are computed by evaluating the overlap and orientation agreement between predicted and observed building facades. The likelihood function, $p(\mathcal{J}|g)$, normalizes appearance, geometric, and semantic evidence by taking:

$$p(\mathcal{J}|g) = \frac{S(Z, \hat{Z})}{S_{\max}(Z, \hat{Z})}$$

where $S(Z, \hat{Z})$ accumulates indicator-weighted cosine orientation agreements across image columns, downweighted by distance-based Gaussians, and $S_\text{max}$ sets the normalization against missing detections or mismatches.

In federated scenarios, local agents can compute such likelihood distributions independently, exchanging only summary statistics or probability maps for global pose aggregation—facilitating privacy-preserving, low-overhead decision fusion.

3. Distributed and Federated Optimization Frameworks

Distributed optimization methods tackle the non-convexity and robustness constraints in federated localization, particularly in environments with outlier data or noisy measurements. L₁-norm robust formulations (Mirzaeifard et al., 2023), where each node $i$ aims to minimize

$$\hat{x} = \arg\min_x \frac{1}{L} \sum_{i=1}^L |\|x - a_i\| - d_i|$$

mitigate the influence of non-Gaussian outliers, using consensus averaging and distributed sub-gradient descent. The iterative scheme consists of a diffusion step—averaging estimates with neighbors—and a non-smooth sub-gradient update with explicit formulae for managing absolute-value cost functions.

Further advances employ Federated Smoothing ADMM (Mirzaeifard et al., 12 Mar 2025), decomposing the problem into a difference-of-convex (DC) objective, smoothing non-smooth components via Moreau envelope approximations, and supporting asynchronous client updates. This algorithm ensures convergence to stationary points under practical federated settings with varying client computation schedules and supports resilience to high outlier rates.

4. Privacy-Preserving Model Architectures and Data Sharing

Hierarchical and similarity-aware federated learning strategies address privacy, scalability, and data heterogeneity in indoor localization (Jan et al., 2 Jul 2025, Jaheen et al., 2 Aug 2025). Hierarchical FL enables multi-level model aggregation (e.g., floor, building, global), with each node transmitting only model weights—not raw fingerprints. The global optimization minimizes a weighted sum of local losses:

$$\mathcal{L}(X; \phi) = \sum_{k=1}^K w_k \mathcal{L}_k(X_k; \phi)$$

Similarity-aware aggregation (Jaheen et al., 2 Aug 2025) further clusters clients whose updates exhibit high gradient alignment, ensuring that only similar distributions are merged, thus reducing adverse effects of non-IID data and yielding superior classification accuracy (e.g., 92.89% on UJIIndoorLoc).

Personalized federated schemes in cross-view geo-localization (Anagnostopoulos et al., 7 Nov 2024) permit selective sharing of coarse feature extractor parameters while local fine-grained features remain private. This reduces communication overhead, supports adaptation to heterogeneous environments, and maintains accuracy close to centralized training even as data distributions diverge.

5. Topological, Semantic, and Foundation Model-Based Localization

Graph-based localization networks (Niwa et al., 2022) extend the paradigm by combining spatial consistency in topological maps with temporal consistency from image time series. Agglomeration of feature embeddings via Graph Isomorphism Networks (GIN) and LSTM cell states provides resilience in environments with repeated visual cues.

Semantic and foundation model-based methods (Mirjalili et al., 2023) leverage large pretrained visual-LLMs (CLIP) and LLMs (GPT-3) to extract high-level object and room labels, constructing image descriptors robust to severe scene changes and viewpoint variation. The semantic similarity score for localization is computed as a sum of object and room similarities, rewarding consistent semantic content between query and database images, and readily adaptable for federated systems by transmitting only abstracted descriptors.

6. Federated System Architectures, Service Discovery, and Real-World Applications

At scale, federated localization platforms such as OpenFLAME (Bharadwaj et al., 6 Nov 2024, Bharadwaj et al., 4 Oct 2025) organize independent map servers (hosting 3D scans, visual positioning databases) into a unified service via DNS-based service discovery coupled with geo-domain computation. Each device queries for map servers covering its region, submits sensor cues (images, odometry), and receives pose estimates in local coordinates. The use of waypoints as cross-map anchors, and the transformation:

$W_A = P_A \cdot (P_R)^{-1} \cdot W_R$

allows applications (e.g., AR navigation) to operate seamlessly across domains with differing local coordinate systems.

Quality control, service selection (via CLIP-powered place recognizers), and dynamic stitching of pose trajectories ensure coherent, privacy-controlled localization throughout federated networks. Such architectures support incremental deployability, efficiency, privacy, and adaptability relative to traditional centralized VPS solutions, which often lack coverage in private indoor spaces and pose privacy risks.

7. Evaluation Metrics, Generalization, and Limitations

Performance is commonly measured via metrics such as mean absolute error (for indoor localization), recall at rank-1 (for place recognition), and pose/rotation errors (for camera localization). In federated frameworks, preservation of data privacy, reduction of bandwidth usage, and robustness to statistical data heterogeneity are critical. Systems such as FastForward (Barroso-Laguna et al., 1 Oct 2025) demonstrate that feed-forward, transformer-based architectures can generalize across unseen domains and achieve rapid, scalable localization with minimal preparation.

Potential limitations include increased complexity in map server discovery, coordinate system consistency, and client-side filtering overhead in federated services. However, innovations in model aggregation, semantic abstraction, and decentralized protocol design continue to advance federated image-based localization as a technically rigorous, robust, and scalable solution for contemporary location-aware applications.